{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import time\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "pd.set_option(\"display.max_columns\",None) #显示所有列\n",
    "pd.set_option('display.max_rows', None)  #显示所有行\n",
    "pd.set_option('display.width',2000) #设置显示宽度\n",
    "plt.rcParams['font.sans-serif']=['Microsoft YaHei'] # 正常显示中文字体\n",
    "plt.style.use('seaborn-notebook') #选择美化样式 ['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n",
    "sns.set_style({'font.sans-serif':['simhei','Arial']}) #设置字体风格\n",
    "sns.set()#切换到seaborn的默认运行配置\n",
    "from matplotlib.font_manager import FontProperties\n",
    "myfont=FontProperties(fname=r'C:\\Windows\\Fonts\\simhei.ttf',size=14)\n",
    "sns.set(font=myfont.get_name())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149 entries, 0 to 148\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   岗位关键字   149 non-null    object\n",
      " 1   岗位诱惑    149 non-null    object\n",
      " 2   岗位名     149 non-null    object\n",
      " 3   城市区域    149 non-null    object\n",
      " 4   一般要求    149 non-null    object\n",
      " 5   薪资水平    149 non-null    object\n",
      " 6   公司名     149 non-null    object\n",
      " 7   公司信息    149 non-null    object\n",
      "dtypes: object(8)\n",
      "memory usage: 9.4+ KB\n"
     ]
    }
   ],
   "source": [
    "rowdata=pd.read_csv(\"data.csv\",encoding='gbk')\n",
    "data=rowdata.copy()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析:数据导入之后一共有8列属性，但是某些属性粒度切割不够细（例如：公司信息包含公司类型、融资阶段、规模等三个子信息；一般要求中又包括薪资、经验与学历），因此需要对数据进行进一步处理，以便更好分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>岗位名</th>\n",
       "      <th>岗位关键字</th>\n",
       "      <th>城市</th>\n",
       "      <th>区域</th>\n",
       "      <th>经验</th>\n",
       "      <th>学历</th>\n",
       "      <th>最高工资(k)</th>\n",
       "      <th>最低工资(k)</th>\n",
       "      <th>岗位诱惑</th>\n",
       "      <th>公司名</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>融资阶段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>SQL 数据库 数据运营</td>\n",
       "      <td>成都</td>\n",
       "      <td>高新区</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>“大厂 团队管理 上升快 环境nice”</td>\n",
       "      <td>快手</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>D轮及以上</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>深圳</td>\n",
       "      <td>车公庙</td>\n",
       "      <td>1-3年</td>\n",
       "      <td>本科</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>“五险一金,绩效奖金,带薪年假,员工旅游”</td>\n",
       "      <td>平安产险</td>\n",
       "      <td>金融</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>上市公司</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据分析师/数据科学家</td>\n",
       "      <td>数据分析 数据挖掘 算法</td>\n",
       "      <td>北京</td>\n",
       "      <td>中关村</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>硕士</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>“薪酬福利优,领导靠谱,成长空间”</td>\n",
       "      <td>商汤科技</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>C轮</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据分析师</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>西二旗</td>\n",
       "      <td>不限</td>\n",
       "      <td>本科</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>“高薪福利；行业独角兽；”</td>\n",
       "      <td>快手</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>D轮及以上</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>数据分析师（业务分析）-【商业化】</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>北京</td>\n",
       "      <td>西二旗</td>\n",
       "      <td>3-5年</td>\n",
       "      <td>本科</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>“扁平化管理”</td>\n",
       "      <td>快手</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>D轮及以上</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 岗位名         岗位关键字  城市   区域    经验   学历  最高工资(k)  最低工资(k)                   岗位诱惑   公司名   公司类型     公司规模   融资阶段\n",
       "0               数据分析  SQL 数据库 数据运营  成都  高新区  1-3年   本科       10        8   “大厂 团队管理 上升快 环境nice”    快手  文娱丨内容  2000人以上  D轮及以上\n",
       "1              数据分析师          数据分析  深圳  车公庙  1-3年   本科       15       11  “五险一金,绩效奖金,带薪年假,员工旅游”  平安产险     金融  2000人以上   上市公司\n",
       "2        数据分析师/数据科学家  数据分析 数据挖掘 算法  北京  中关村  3-5年   硕士       40       20      “薪酬福利优,领导靠谱,成长空间”  商汤科技   人工智能  2000人以上     C轮\n",
       "3              数据分析师          数据分析  北京  西二旗    不限   本科       40       20          “高薪福利；行业独角兽；”    快手  文娱丨内容  2000人以上  D轮及以上\n",
       "4  数据分析师（业务分析）-【商业化】          数据分析  北京  西二旗  3-5年   本科       40       20                “扁平化管理”    快手  文娱丨内容  2000人以上  D轮及以上"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"经验\"]=data[\"一般要求\"].str.split(\"/\",expand=True)[0]\n",
    "data[\"学历\"]=data[\"一般要求\"].str.split(\"/\",expand=True)[1]\n",
    "data[\"经验\"]=data[\"一般要求\"].str.split(\" \",expand=True)[1]\n",
    "data.drop(\"一般要求\",axis=1,inplace=True)\n",
    "data[\"城市\"]=data[\"城市区域\"].str.split(\"·\",expand=True)[0]\n",
    "data[\"区域\"]=data[\"城市区域\"].str.split(\"·\",expand=True)[1]\n",
    "data.drop(\"城市区域\",axis=1,inplace=True)\n",
    "data[\"最低工资(k)\"]=data[\"薪资水平\"].str.split(\"-\",expand=True)[0]\n",
    "data[\"最低工资(k)\"]=data[\"最低工资(k)\"].apply(lambda x:int(x.replace(\"k\",\"\")))\n",
    "data[\"最高工资(k)\"]=data[\"薪资水平\"].str.split(\"-\",expand=True)[1]\n",
    "data[\"最高工资(k)\"]=data[\"最高工资(k)\"].apply(lambda x:int(x.replace(\"k\",\"\")))\n",
    "data.drop(\"薪资水平\",axis=1,inplace=True)\n",
    "data[\"公司类型\"]=data[\"公司信息\"].str.split(\" / \",expand=True)[0]\n",
    "data[\"公司规模\"]=data[\"公司信息\"].str.split(\" / \",expand=True)[2]\n",
    "data[\"融资阶段\"]=data[\"公司信息\"].str.split(\" / \",expand=True)[1]\n",
    "data.drop(\"公司信息\",axis=1,inplace=True)\n",
    "data[\"经验\"]=data[\"经验\"].apply(lambda x:x.replace(\"经验\",\"\"))\n",
    "data=data[[\"岗位名\",\"岗位关键字\",\"城市\",\"区域\",\"经验\",\"学历\",\"最高工资(k)\",\"最低工资(k)\",\"岗位诱惑\"\n",
    "           ,\"公司名\",\"公司类型\",\"公司规模\",\"融资阶段\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    城市  岗位需求量\n",
      "0   北京     58\n",
      "1   上海     25\n",
      "2   广州     21\n",
      "3   深圳     18\n",
      "4   杭州      7\n",
      "5   成都      4\n",
      "6   苏州      3\n",
      "7   合肥      3\n",
      "8   南京      2\n",
      "9   天津      2\n",
      "10  济南      1\n",
      "11  武汉      1\n",
      "12  重庆      1\n",
      "13  厦门      1\n",
      "14  长沙      1\n",
      "15  青岛      1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制非对称子图\n",
    "fig1=plt.figure() #设置图窗口\n",
    "ax1=plt.subplot2grid((2,2),(0,0)) #设置第一张子图，位置0,0\n",
    "ax2=plt.subplot2grid((2,2),(0,1))  #设置第二张子图，位置0,1\n",
    "ax3=plt.subplot2grid((2,2),(1,0),colspan=2)  #设置第三张子图，位置1,0开始，列跨度为2\n",
    "#plt.subplots_adjust(wspace=0.1, hspace=0.2)  # 调整子图间距\n",
    "\n",
    "sns.barplot(x='城市', y='最低工资(k)', palette=\"Blues_d\", data=data.sort_values(\"最低工资(k)\",ascending=False), ax=ax1)\n",
    "ax1.tick_params(axis='x',labelsize=6)\n",
    "ax1.tick_params(axis='y',labelsize=6)\n",
    "ax1.set_xlabel('城市',fontsize=10)\n",
    "ax1.set_ylabel('最低工资(k)',fontsize=10)\n",
    "\n",
    "sns.barplot(x='城市', y='最高工资(k)', palette=\"Greens_d\", data=data, ax=ax2)\n",
    "ax2.tick_params(axis='x',labelsize=6)\n",
    "ax2.tick_params(axis='y',labelsize=6)\n",
    "ax2.set_xlabel('城市',fontsize=10)\n",
    "ax2.set_ylabel('最高工资(k)',fontsize=10)\n",
    "\n",
    "data_city=data[\"城市\"].value_counts().sort_values(ascending=False).to_frame().reset_index()\n",
    "data_city.rename(columns=({'城市':'岗位需求量','index':'城市'}),inplace=True)\n",
    "print(data_city)\n",
    "# sns.countplot(data['城市'], palette=\"Reds_d\", ax=ax3)\n",
    "sns.barplot(x=data_city[\"城市\"],y=data_city[\"岗位需求量\"],palette=\"Reds_d\", data=data_city, ax=ax3)#\n",
    "ax3.tick_params(axis='x',labelsize=6)\n",
    "ax3.tick_params(axis='y',labelsize=6)\n",
    "ax3.set_xlabel('城市',fontsize=10)\n",
    "ax3.set_ylabel('岗位需求量',fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "value=[['北京', 58],\n",
    " ['上海', 25],\n",
    " ['广东', 39],\n",
    " ['浙江', 7],\n",
    " ['四川', 4],\n",
    " ['江苏', 5],\n",
    " ['安徽', 3],\n",
    " ['天津', 2],\n",
    " ['河北', 1],\n",
    " ['湖北', 1],\n",
    " ['重庆', 1],\n",
    " ['福建', 1],\n",
    " ['湖南', 1],\n",
    " ['山东', 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/echarts.min.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "new Promise(function(resolve, reject) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    script.onload = resolve;\n",
       "    script.onerror = reject;\n",
       "    script.src = \"https://assets.pyecharts.org/assets/maps/china.js\";\n",
       "    document.head.appendChild(script);\n",
       "}).then(() => {\n",
       "\n",
       "});\n",
       "});"
      ],
      "text/plain": [
       "<pyecharts.render.display.Javascript at 0x22fc26fe288>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.globals import CurrentConfig, NotebookType\n",
    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Map()\n",
    "    .add(\"city\", value, \"china\")\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Map-China\"), visualmap_opts=opts.VisualMapOpts(max_=58, is_piecewise=True)\n",
    "    )\n",
    "\n",
    ")\n",
    "c.load_javascript()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "<head>\n",
       "    <meta charset=\"UTF-8\">\n",
       "</head>\n",
       "<body>\n",
       "        <div id=\"659b7867c63a4ee1910cf75bef403f33\" class=\"chart-container\" style=\"width:900px; height:500px;\"></div>\n",
       "    <script>\n",
       "        var chart_659b7867c63a4ee1910cf75bef403f33 = echarts.init(\n",
       "            document.getElementById('659b7867c63a4ee1910cf75bef403f33'), 'white', {renderer: 'canvas'});\n",
       "        var option_659b7867c63a4ee1910cf75bef403f33 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"map\",\n",
       "            \"name\": \"city\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"mapType\": \"china\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\",\n",
       "                    \"value\": 58\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e0a\\u6d77\",\n",
       "                    \"value\": 25\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u4e1c\",\n",
       "                    \"value\": 39\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d59\\u6c5f\",\n",
       "                    \"value\": 7\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\",\n",
       "                    \"value\": 4\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\",\n",
       "                    \"value\": 5\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u5fbd\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5929\\u6d25\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5317\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e56\\u5317\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u91cd\\u5e86\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u798f\\u5efa\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e56\\u5357\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c71\\u4e1c\",\n",
       "                    \"value\": 1\n",
       "                }\n",
       "            ],\n",
       "            \"roam\": true,\n",
       "            \"zoom\": 1,\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"city\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"city\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Map-China\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 58,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 14,\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "        chart_659b7867c63a4ee1910cf75bef403f33.setOption(option_659b7867c63a4ee1910cf75bef403f33);\n",
       "    </script>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x22fc2a1bcc8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：从岗位需求来看，“北上广深杭”等互联网大城市对数据分析师需求最为旺盛，从地区分布来看，主要集中在长三角、珠三角、北京、成都等经济发达地区；从薪资来看，薪资水平与当地需求量呈正相关关系，其中杭州的最低工资差异较大，竞争较为激烈而北京的最低工资、最高工资均高于其他城市，且其祈福不大，说明市场较为稳定。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学历与经验分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         需求比例\n",
      "学历           \n",
      " 本科  0.865772\n",
      " 硕士  0.060403\n",
      " 不限  0.040268\n",
      " 大专  0.033557\n"
     ]
    }
   ],
   "source": [
    "mypal=sns.diverging_palette(200, 20, l=40, n=4)\n",
    "fig2=plt.figure() #设置图窗口\n",
    "ax1=plt.subplot2grid((2,1),(0,0))\n",
    "ax2=plt.subplot2grid((2,1),(1,0))\n",
    "# ax3=plt.subplot2grid((2,2),(1,0))\n",
    "# ax4=plt.subplot2grid((2,2),(1,1))\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.2)  # 调整子图间距\n",
    "df_aca=data.groupby(\"学历\")[\"区域\"].count().sort_values(ascending=False).to_frame().reset_index()\n",
    "sns.barplot(y=\"学历\",x=\"区域\",data=df_aca.head(20),ax=ax1,orient=\"h\",palette=mypal)\n",
    "ax1.set_xlabel(\"需求量\",fontsize=10)\n",
    "ax1.set_ylabel(\"学历\",fontsize=10)\n",
    "df_exp=data.groupby(\"经验\")[\"区域\"].count().sort_values(ascending=False).to_frame().reset_index()\n",
    "sns.barplot(x='经验', y='区域', palette=mypal, data=df_exp, ax=ax2)\n",
    "ax2.tick_params(axis='y',labelsize=6)\n",
    "ax2.set_xlabel('经验',fontsize=10)\n",
    "ax2.set_ylabel('需求量',fontsize=10)\n",
    "plt.show()\n",
    "\n",
    "df_aca2=pd.DataFrame()\n",
    "df_aca2[\"需求比例\"]=df_aca[\"区域\"]/sum(df_aca[\"区域\"])\n",
    "df_aca2.index=df_aca[\"学历\"]\n",
    "print(df_aca2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：目前数据分析岗位对于学历的要求绝大多数要求“本科以上”而其次是“硕士以上”，可见这一岗位专业性较强，对于学历存在一定门槛；从经验上来看，市场主要需求集中在“3~5”以及“1~3”年的职场人士，在这个经验层内相对掌握的业务知识和技术比较扎实，适用性最强，而“一年以下”（含应届毕业生）的需求很少，说明数据分析岗位十分看重求职者的工作经验，因此对于应届毕业生应该多多实习，积攒经验。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 薪资分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3,[[ax1,ax2,ax3],[ax4,ax5,ax6]]=plt.subplots(2,3)\n",
    "sns.boxplot(x='学历', y='最高工资(k)', data=data, ax=ax1,palette=mypal)\n",
    "ax1.tick_params(axis='x',labelsize=8)\n",
    "ax1.tick_params(axis='y',labelsize=8)\n",
    "ax1.set_xlabel('学历',fontsize=10)\n",
    "ax1.set_ylabel('最高工资',fontsize=10)\n",
    "sns.distplot(data[\"最高工资(k)\"],ax=ax2,bins=20) #面积分布情况（直方图 ）\n",
    "sns.kdeplot(data[\"最高工资(k)\"],shade=True,ax=ax2)#生成核密度图\n",
    "sns.boxplot(x='学历', y='最低工资(k)', data=data, ax=ax4,palette=mypal)\n",
    "ax4.tick_params(axis='x',labelsize=8)\n",
    "ax4.tick_params(axis='y',labelsize=8)\n",
    "ax4.set_xlabel('学历',fontsize=10)\n",
    "ax4.set_ylabel('最低工资',fontsize=10)\n",
    "\n",
    "sns.distplot(data[\"最低工资(k)\"],ax=ax5,bins=20) #面积分布情况（直方图 ）\n",
    "sns.kdeplot(data[\"最低工资(k)\"],shade=True,ax=ax5)#生成核密度图\n",
    "\n",
    "sns.boxplot(x='经验', y='最高工资(k)', data=data, ax=ax3,palette=mypal)\n",
    "ax3.tick_params(axis='x',labelsize=7)\n",
    "ax3.tick_params(axis='y',labelsize=7)\n",
    "ax3.set_xlabel('经验',fontsize=10)\n",
    "ax3.set_ylabel('最高工资',fontsize=10)\n",
    "\n",
    "sns.boxplot(x='经验', y='最低工资(k)', data=data, ax=ax6,palette=mypal)\n",
    "ax6.tick_params(axis='x',labelsize=7)\n",
    "ax6.tick_params(axis='y',labelsize=7)\n",
    "ax6.set_xlabel('经验',fontsize=10)\n",
    "ax6.set_ylabel('最低工资',fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：从统计结果来看，最高工资与最低工资的均值分别为24.26k以及14.31k，最高工资的集中度较高，集中在2030k之间，而最低工资则相对分散，10、15、20k三个位置较多，薪资水平差异较大，说明薪资的多少不仅仅取决所在的行业背景，在很大程度上与求职者的经验、技能、学历、所在城市有显著关系。从学历来看，硕士的薪资水平（最大值、最小值、平均值）均高于本科学历；经验上来看，510年经验的薪水较高，综合来看，薪资水平随学历以及经验呈现正相关关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司信息分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4,[ax1,ax2]=plt.subplots(1,2)\n",
    "sns.countplot(data['公司规模'], palette=mypal, ax=ax1)\n",
    "ax1.tick_params(axis='y',labelsize=9)\n",
    "ax1.tick_params(axis='x',labelsize=9)\n",
    "ax1.set_xlabel('公司规模',fontsize=11)\n",
    "ax1.set_ylabel('需求量',fontsize=11)\n",
    "\n",
    "data[\"融资阶段\"]=data[\"融资阶段\"].apply(lambda x:x.replace(\"不需要融资\",\"未融资\"))\n",
    "sns.countplot(data['融资阶段'], palette=mypal, ax=ax2)\n",
    "ax2.tick_params(axis='y',labelsize=9)\n",
    "ax2.tick_params(axis='x',labelsize=9)\n",
    "ax2.set_xlabel('融资阶段',fontsize=11)\n",
    "ax2.set_ylabel('需求量',fontsize=11)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：从结果来看，大型公司以及融资末阶段、上市公司对数据分析的需求较大，说明数据分析岗在大公司的重视程度比较高，其前景看好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels1 = data[\"城市\"].unique().tolist()  # tolist()将数组array变为列表list\n",
    "data[\"城市\"] = data[\"城市\"].apply(lambda x: labels1.index(x))\n",
    "labels2 = data[\"经验\"].unique().tolist()  # tolist()将数组array变为列表list\n",
    "data[\"经验\"] = data[\"经验\"].apply(lambda x: labels2.index(x))\n",
    "labels3 = data[\"学历\"].unique().tolist()  # tolist()将数组array变为列表list\n",
    "data[\"学历\"] = data[\"学历\"].apply(lambda x: labels3.index(x))\n",
    "labels4 = data[\"公司规模\"].unique().tolist()  # tolist()将数组array变为列表list\n",
    "data[\"公司规模\"] = data[\"公司规模\"].apply(lambda x: labels4.index(x))\n",
    "labels5= data[\"融资阶段\"].unique().tolist()  # tolist()将数组array变为列表list\n",
    "data[\"融资阶段\"] = data[\"融资阶段\"].apply(lambda x: labels5.index(x))\n",
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(10,10))\n",
    "# plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(data.corr(),linewidths=0.1,vmax=1.0, square=False,\n",
    "            cmap=colormap, linecolor='white', annot=True,annot_kws={'size':10,'color':'black'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：从相关性来看，公司融资阶段与公司规模相关、最高工资与城市、公司规模以及个人经验相关性更大而与学历的相关程度并不高，因此一个人工资上限并不被学历出身所束缚，但最低工资与学历的相关程度远大于最高工资与学历的相关程度，因此学历在很大程度上能够决定求职者薪资的下限。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文从数据分析的视角分析了当前市场上对于“数据分析”的需求情况，得到如下结论：\n",
    "* 地域需求方面，岗位需求主要集中在经济发达地区（长三角、珠三角以及北京），在城市上集中于互联网大城市“北上广深杭”，其中北京市场相对成熟，杭州可变性较大；\n",
    "* 岗位要求方面，集中在“本科以上学历”，“1~3”年工作经验，技能上主要以Sql等数据分析技能为主，同时补充一些“电商、生活消费、金融”等领域的业务知识，对于应届毕生生而言需求较少，竞争压力更大，因此在校生应该修炼基础技能、拓广业务知识、多实习积攒经验提高竞争力；\n",
    "* 薪资方面，工资与城市、公司规模以及个人经验相关性大，且呈正相关关系；学历在很大程度能够决定求职者薪资的下限而不决定上限。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
